Leave No Trace

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Many hikers, campers, and other outdoor enthusiasts know the saying, “Leave No Trace”. The National Park Service publishes websites and physical cards explaining the principles of Leave No Trace (2022). The seven principles are:
      - Plan Ahead and Prepare
      - Travel and Camp on Durable Surfaces
      - Dispose of Waste Properly
      - Leave What You Find
      - Minimize Campfire Impacts
      - Respect Wildlife
      - Be Considerate of Other Visitors
      (2022)

You can find more information about the principles here. While everyone exploring national parks and nature should know them all, the idea is to leave things the way you found them and ensure others can enjoy natural spaces as you did after you left.

Unfortunately, we live in a world where people affect change in areas beyond where they live and visit. Industry changes have impacted the climate and conditions on the other side of the world. We can no longer ensure that future generations will be able to enjoy the outdoors like we have without making some changes.

Sipapu Bridge, NPS photo by Jacob W. Frannk (2024)

Predicted Data

This page uses data from predicted data. The projected data is split into two terms: near-term from 2021 to 2059 and long-term from 2060 to 2099. The near-term and long-term data were predicted using two Representative Concentration Pathways (RCPs) and twenty General Circulation Model (GCM) scenarios (2022). The two RCPs are based on a medium mitigation and stabilization scenario, RCP4.5, and a high baseline, no-policy scenario, RCP8.5 (2022). The twenty GCM scenarios are built based on models from SOILWAT2. You can find more information on the models here. (2023). The predicted data shown in the plots below are from the twenty GCMs are averaged together to give a single value for each observation for the RCP4.5 and RCP8.5 predictions.

Water Availability

All life on Earth needs water to survive. In this section, we will compare annual precipitation and the amount of water available in the top 50 cm of soil. These two features should provide valuable insights into whether water is available to animals and plants in the area.

Precipitation

The three plots below show the average annual precipitation among all observation sites for the historical data, predicted near-term data and predicted long-term data. Averaging the twenty models together reduces the variance in the predicted data, leading to a higher mean annual precipitation and lower variance than the historical data. Based on these charts and a look at the basic statistics during EDA. The future will have little impact on the amount of precipitation.

Soil Water Availability

Once again, the data we saw in EDA and these plots suggest that there will be plenty of water available for plants and animals in the future. To be sure, we will use Welch’s T-Test to compare the three different data sets.

T-Test

Here, we use Welch’s T-Test, which compares historical data to near term and long term data. Below are our null and alternative hypotheses.

\(H_0:\) There is no difference in soil water availability between historical and near/long term data.

\(H_A\):$ There is a difference in soil water availability between historical and near/long term data.

The t-statistics for both tests shown below have very small p-values, indicating strong evidence to reject the null hypotheses. These suggest that there is a significant difference in soil water availability. The 95 percent confidence for the true difference in mean reveals that the predicted near and long term data has a higher soil water availability.

%%R
#import libray
library(tidyverse)
#import data
df <- read.csv('../data/data.csv')
#filter data
hist <- df[df$TimePeriod == "Hist", ]
nt <- df[df$TimePeriod == "NT", ]
lt <- df[df$TimePeriod == "LT", ]
#test hist vs near term
print("Welch's T-Test: Historical vs Near Term Data")
print(t.test(hist$SWA_Fall_top50, nt$SWA_Fall_top50, alternative = "two.sided"))
#test hist vs lt
print("Welch's T-Test: Historical vs Long Term Data")
print(t.test(hist$SWA_Fall_top50, lt$SWA_Fall_top50, alternative = "two.sided"))

[1] "Welch's T-Test: Historical vs Near Term Data"

    Welch Two Sample t-test

data:  hist$SWA_Fall_top50 and nt$SWA_Fall_top50
t = -11.034, df = 4723.2, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.2174275 -0.1518233
sample estimates:
mean of x mean of y 
 1.348573  1.533198 

[1] "Welch's T-Test: Historical vs Long Term Data"

    Welch Two Sample t-test

data:  hist$SWA_Fall_top50 and lt$SWA_Fall_top50
t = -13.273, df = 4719.9, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.2548388 -0.1892461
sample estimates:
mean of x mean of y 
 1.348573  1.570615 

Temperature

Annual Mean Temperature

T-Test

%%R
#test hist vs near term
print("Welch's T-Test: Historical vs Near Term Data")
print(t.test(hist$T_Annual, nt$T_Annual, alternative = "two.sided"))
#test hist vs lt
print("Welch's T-Test: Historical vs Long Term Data")
print(t.test(hist$T_Annual, lt$T_Annual, alternative = "two.sided"))

[1] "Welch's T-Test: Historical vs Near Term Data"

    Welch Two Sample t-test

data:  hist$T_Annual and nt$T_Annual
t = -178.1, df = 4928.7, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -2.151217 -2.104373
sample estimates:
mean of x mean of y 
 10.83586  12.96366 

[1] "Welch's T-Test: Historical vs Long Term Data"

    Welch Two Sample t-test

data:  hist$T_Annual and lt$T_Annual
t = -317.17, df = 5583.6, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.933786 -3.885456
sample estimates:
mean of x mean of y 
 10.83586  14.74548 

T-Test

%%R
#test hist vs near term
print("Welch's T-Test: Historical vs Near Term Data")
print(t.test(hist$T_Annual, nt$Tmax_Summer, alternative = "two.sided"))
#test hist vs lt
print("Welch's T-Test: Historical vs Long Term Data")
print(t.test(hist$T_Annual, lt$Tmax_Summer, alternative = "two.sided"))

[1] "Welch's T-Test: Historical vs Near Term Data"

    Welch Two Sample t-test

data:  hist$T_Annual and nt$Tmax_Summer
t = -2166.3, df = 6268.2, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -27.51496 -27.46521
sample estimates:
mean of x mean of y 
 10.83586  38.32595 

[1] "Welch's T-Test: Historical vs Long Term Data"

    Welch Two Sample t-test

data:  hist$T_Annual and lt$Tmax_Summer
t = -2158.5, df = 8423.9, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -29.5394 -29.4858
sample estimates:
mean of x mean of y 
 10.83586  40.34847 

Conclusion

here is the conclusion

References

Ecology, Dryland. 2023. “SOILWAT2.” October 24, 2023. https://github.com/DrylandEcology/SOILWAT2.

Service, National Park. 2022. “Leave No Trace Seven Principles.” May 16, 2022. https://www.nps.gov/articles/leave-no-trace-seven-principles.htm.

———. 2024. “Natural Bridges.” April 25, 2024. https://www.nps.gov/nabr/index.htm.

Survey, U. S. Geological. 2022. “Climate and Drought Adaptation: Historical and Projected Future Exposure Metrics for Southeastern Utah Group National Parks.” March 2, 2022. https://www.sciencebase.gov/catalog/file/get/61a6952fd34eb622f6978d9f?f=__disk__78%2F99%2F9b%2F78999b749568b2fbba86ce5dc9fc89aebe469388&transform=1&allowOpen=true.